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Increasing demand for open-source software (OSS) has raised the value of efficient selection in terms of quality; usability is an essential quality factor that significantly affects system acceptability and sustainability. Most large and complex software packages partitioned across multiple portals and involve many users — each with their role in the software package; those users have different perspectives on the software package, defined by their knowledge, responsibilities, and commitments. Thus, a multi-perspective approach has been used in usability evaluation to overcome the challenge of inconsistency between users’ perspectives; the inconsistency challenge would lead to an ill-advised decision on the selection of a suitable OSS. This study aimed to assist the public and private organizations in evaluating and selecting the most suitable OSS. The evaluation of the OSS software packages to choose the best one is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, and (c) data variation; thus, it is considered a sophisticated multi-criteria decision making (MCDM) problem; moreover, the multi-perspective usability evaluation framework for OSS selection lacks in the current literature. Hence, this study proposes a novel multi-perspective usability evaluation framework for the selection of OSS based on the multi-criteria analysis. Integration of best-worst method (BWM) and VIKOR MCDM techniques has been used for weighting and ranking OSS alternatives. BWM is utilized for weighting of evaluation criteria, whereas VIKOR is applied to rank OSS-LMS alternatives. Individual and group decision-making contexts, and the internal and external groups aggregation were used to demonstrate the efficiency of the proposed framework. A well-organized algorithmic procedure is presented in detail, and a case study was examined to illustrate the validity and feasibility of the proposed framework. The results demonstrated that BWM and VIKOR integration works effectively to solve the OSS software package benchmarking/selection problems. Furthermore, the ranks of OSS software packages obtained from the VIKOR internal and external group decision making were similar; the best OSS-LMS based on the two ways was ‘Moodle’ software package. Among the scores of groups in the objective validation, significant differences were identified; this indicated that the ranking results of internal and external VIKOR group decision making were valid, which pointed to the validation of the framework.

Educational systems aim to help students discover their talents and develop a passion for learning. Hence, it is supposed that the education systems facilitate the efforts to motivate students. Hence, it is crucial to model students’ motivation to develop such a motivation in a concerned educational system. However, to date, there are limited number of works reporting on motivation model in this area. In this light, most of previous works have showed that students are evaluated only by the students’ test scores, not by students’ attitudes or behaviors. On the other hand, companies have modeled the motivation based on employees’ behaviors as explained by “Expectancy Theory” and have implemented it into a system called “Reward and Punishment”. In fact, the implementation at companies has shown a great influence in improving employees’ skills as they are motivated by all other employees. Hence, this research focused on modeling students’ motivation by students’ attitudes or behaviors. The reward and punishment are then used to comprehend this model. Thus, rather than by test scores, students will be evaluated by students’ attitudes toward positive attitudes rather than negative attitudes. It is expected that appropriate reward and punishment for modeling motivation will improve students’ motivation. For this reason, this paper proposed to construct an evaluation criterion by modeling students’ motivation towards positive attitudes and behaviors. Students’ attitudes will be evaluated and formulated into a motivation model. This model then will be tested in a normal existing classroom with a scenario using reward and punishment. Applying reward and punishment to existing classroom systems is expected to motivate students in improving their attitudes and behaviors.

Objectives: This study aimed to design an application that effectively scans, detects, and removes malware based on their signatures and behaviours. Methods/Statistical analysis: The rapid growth in the number and types of malware poses high security risks despite the numerous antivirus softwares with Signature-Based Detection (SBD) method. The SBD method depends on the signatures or malware names that are available in the algorithm database. Findings: Malware is a type of malicious software that poses security threats to the targeted system, resulting in information loss, resource abuse, or system damage. The antivirus software is one of the most commonly used security tools to detect and remove malware. However, the malware defences should focus on the malware signatures since there is no universal way of recognising all malware. Therefore, this study suggested N/A detection technique as the dynamic method (behaviour-based detection method) that depends on the Windows Registry (system database). Both static and dynamic detection methods were assessed in this study. Based on the experimental outcomes, SBD method detected and removed most of malware (only known viruses). Application/Improvements: Meanwhile, the N/A detection method detected and removed all injected malware (known and unknown Trojan horse) within a relatively low running time. Keywords: Dynamic Method, Malicious Software, Malware Detection, Signature Analysis, Static Method

Introduction:type 2 diabetes mellitus has become a global public health crisis. The increment in the cases has contributed significantly to the parallel increase in the prevalence of overweight and obesity. This paper aimed to analyse the relationship between lipid profile, waist circumference and body mass index (BMI) with the glycaemic control of the diabetes patients in Kedah. Methods:a cross-sectional study was conducted, using the Kedah audit samples data extracted from the National Diabetes Registry (NDR) from the year 2014 to 2018. A total of 25,062 registered type 2 diabetes mellitus patients were selected using the inclusion and exclusion criteria from the registry. Only patients with complete data on their HbA1C, lipid profile, waist circumference and BMI were analysed using SPSS version 21. Results:the means for the age, BMI and waist circumference of the samples were 61.5 (±10.85) years, 27.3 (±5.05) kg/m2and 89.46 (±13.58) cm, respectively. Poor glycaemic control (HbA1c>6.5%) was observed in 72.7% of the patients, with females having poorer glycaemic control. The BMI and waist circumference were found to be significantly associated with glycaemic control (P

The increase in the number of open source software (OSS) users have drawn attention to improving usability. Usability is a clear concept that encompassing both task and user characteristics as well as functionality. Usability is an essential factor that affects user acceptance and OSS sustainability, which is considered as the key to the success of the OSS. To some extent, usability is one concern of the larger issue of system acceptability and sustainability. Therefore, usability is an important factor that needs to be considered since the software that is not usable is not going to be sustainable. The objective of this paper is to review researchers' efforts to improve, investigate, and evaluate the usability factor that may affect the OSS acceptability and sustainability and map the research scenery from the articles into a comprehensible structured taxonomy, which would help the researchers to identify different research gaps of this field. A survey of the usability in OSS conducted and 6033 studies identified by a search in four scholarly databases using a query that includes the keywords (usability or learnability or efficiency or satisfaction) and (open source software or OSS). A total of 46 studies are selected. By manually searching in ACM, Springer, and Google Scholar five other studies identified, and thus a total of 51 studies were the final set that includes in this paper. Based on research topics, a taxonomy created and divided into four principal categories which improve OSS usability, analyze OSS usability, evaluate OSS usability, and select and adopt OSS. A comprehensive overview and synthesis of these categories are presented as well. This paper contributes to identifying the possible opportunities and gaps for enabling the participation of interested researchers in this research area. And give possibilities for extending the use of usability research and practices to create more sustainable software. Also, helps in selecting suitable OSS among the alternatives.

Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these techniques are made based on the assumption that the faults are caused by a single fault. However, in a reality, one fault could also possibly caused by multiple faults. This requires a technique which is specifically designed to handle multiple faults. With this regards, application of mutation analysis to localize faults might generate a vast number of mutants. As a result, these will lead to difficulty in choosing important mutants that are capable of localizing faults. Therefore, there is a need for a technique which able to localize a fault effectively with less number of mutants generated. Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. Since both had their own advantage, we have combined both techniques to enhance multiple localization of software fault. The result of the experiment shows that our technique able to detect multiple faults in various java programs but there is a need for improvement especially in prioritize fault that occurs simultaneously.

In the information-oriented society, there are increasing needs to conduct data communication with nearby devices/people. In this light, vibration-based communication method was proposed as one of possible communication means between adjacent devices. This method has been expected to provide an intuitive and safe communication by propagating vibration to a receiver device. This study proposes two types of techniques, which are multi-step ASK (Amplitude Shift Keying) with pseudo clock and PPM (Pulse Position Modulation), to achieve a stable vibration-based communication simply using smart device functions. These proposed techniques are then evaluated through some experiments using several types of smart devices. In addition, the effectiveness of the proposed methods is discussed based on the experimental results.

Trillions of data are being created every day on the Internet due to the growing number of social platforms on the World Wide Web (WWW). Processed data when given in context makes information of any knowledge. However, irresponsible use of the data or misinterpretation of data could be the reasons for false information dissemination. Many researchers from various fields, such as computer science and social science, draw their focus on assessing the veracity of information. There are many techniques to perceive this topic, for instance, social network behaviour, and semantic analysis. The common practice is using semantic analysis approach, where the syntactic structure is analysed and polarity of the texts is determined. In this paper, we approach the veracity assessment by using emotion analysis. We identified emotional states conveyed in news content and calculated the weight of each state in each news content. Contrary to popular belief, our finding showed that emotional, or affective states conveyed in false news are varied - positive and negative states. The distinct feature is the weight of the states in news content. Using multi-layer perceptron, we classified the news and achieved 90% accuracy with our collected dataset and 85% using LIAR dataset.

Many software companies are interested in using Agile methods in their software projects. Contrary to traditional software development methods, Agile methods are people oriented. This fact shows the significant role of individuals in these methods. Increasing prevalence of Agile methods forces software companies to consider people related factors as critical issues in adoption and transition to Agile methods and practices. This article focused on human aspects of Agile transformation process by conducting a Grounded Theory study involving 32 Agile experts. The main contribution of this article is identification and classification of diverse human aspects of Agile transformation process. Analyzing collected data showed that human aspects of Agile transformation process can be classified in several categories. While some of them are impediments to change, some others act as change accelerators. At the same time people’s perceptions about change process and incentive factors can shape and form human aspects during the Agile transformation process. Each of these categories encompasses its own factors which have negative or positive effect on change process.

Recent advances in information technology has made e-learning feasible in many fields of education. Medical education is not any different. This survey is conducted among all the medical educators in Universiti Sains Islam Malaysia (USIM) assessing their readiness and attitudes towards e-learning. A cross-sectional questionnaires assessing the IT skills, Internet usage, attitude, perceived benefits and usage of e-learning among USIM medical lecturers were used. Data gained were then analyzed. The response rate was 92%. Female (56) made majority of the respondents. 44 of the educators aged 25-34 with 56% had teaching experience of

Problem statement: Several authors have proposed information seeking as an appropriate case study for studying software maintenance and evolution that have provided empirical classifications of information seeking in commercial software evolution settings. Approach: However, there is minimal research in the literature describing the information seeking behavior of Open Source programmers, even though Open Source contexts would seem to exacerbate the information seeking problems to a certain extend; where team members are typically delocalized from each other and they are often forced into asynchronous communication. Results: This study reports on an empirical study that classifies Open-Source programmers' information needs generated through open-coding of questions that appear on developers' mailing lists. Based on the generated Information Seeking Schema (ISS), details of the information sought by programmers on 6 different mailing lists over several years are analyzed and discussed. Conclusion/Recommendations: The result shows several interesting findings that describe the programmers' information needs across the mailing lists. Firstly, there are a similar pattern of information artifact and attribute across all projects. Secondly, majority of the programmers' information seeking concentrated on the systems' implementations. Thirdly, the OS programmers have also shown to be team-oriented and they tended to rely on documentation more than what have previously reported. These results suggest the applicability of the ISS in evaluating OS programmers information seeking.

Nowadays, Wi-Fi fingerprinting-based positioning systems provide enterprises the ability to track their various resources more efficiently and effectively. The main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration and the positioning accuracy highly depends on calibration intensity. Unfortunately, calibration procedure requires huge amount of time and effort, and makes large scale deployments of Wi-Fi based indoor positioning systems non-trivial. In this research we present a novel location estimation algorithm for Wi-Fi based indoor positioning systems. The proposed algorithm combines signal sampling and hyperbolic location estimation techniques to estimate the location of mobile users. The algorithm achieves cost-efficiency by reducing the number of fingerprint measurements while providing reliable location accuracy. Moreover, it does not require any additional hardware upgrades to the existing network infrastructure. Experimental results show that the proposed algorithm with easy-to-build signal strength database performs more accurate than conventional signal strength-based methods.

Battery capacity of mobile devices is a critical issue for developing green mobile applications. Therefore, energy efficiency has become a major concern nowadays for energyrestricted embedded system such as smartphones and tablets. In industry, it is a challenge for them to develop an energy efficient product while meeting customer expectation. In previous study, there is lack of method that uses software metrics to measure power consumption of mobile applications. In this paper we have identified several software metrics that can be used as indicator to measure mobile application power consumption. The objective of this study is to which identify software metrics is suitable act as indicators to measure power consumption of mobile applications. This can help mobile software designer to measure power used for their mobile applications in the early design phase. In order to prove this concept, we randomly select two open source mobile applications as our case study. The power used of a mobile application is collected by using Trepn Profiler (Power profiling tool for Qualcomm processor CPU). We capture the actual power consumption and estimated power consumption (without calculate Android OS and profiling tool power) with the profiler. There are overall 18 available metrics based on Object-Oriented Metrics. We map the 18 metrics with the power consumption captured by Trepn Profiler. The results shown that McCabe cyclomatic complexity, number of parameters, nested block depth, weighted methods per class, number of overridden method, number of methods, total lines of code and method lines have significant relationship with power consumption of mobile application. Therefore, these eight metrics can be used as the indicator to measure mobile applications' power consumption.

Maintaining factory default battery endurance rate over time in supporting huge amount of running applications on energy-restricted mobile devices has created a new challenge for mobile applications developer. While delivering customers' unlimited expectations, developers are barely aware of efficient use of energy from the application itself. Thus, developers need a set of valid energy consumption indicators in assisting them to develop energy saving applications. In this paper, we present a few software product metrics that can be used as an indicator to measure energy consumption of Android-based mobile applications in the early of design stage. In particular, Trepn Profiler (Power profiling tool for Qualcomm processor) has used to collect the data of mobile application power consumption, and then analyzed for the 23 software metrics in this preliminary study. 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In this research we propose a new approach of constructing Wi-Fi fingerprints database using Chebyshev wavelet functions. Proposed algorithm combines the reference data collection procedure with path-loss prediction. Our algorithm requires only a few samples to be collected in a given region, and thus significantly reduces the calibration time. Next, the collected reference samples are used to construct area specific path-loss prediction function using Chebyshev wavelets. Once we have the path-loss function, we can predict the signal strength values at any point of the target area. One of the advantages of proposed algorithm is that it does not require any detailed information about target area, i.e. location of wireless access points, number of walls (floors, obstructions) between the transmitter and reference points and etc.

non-peer-reviewed Several authors have proposed information seeking as an appropriate perspective for studying software evolution, and have characterized information seeking empirically in commercial software evolution settings. However, there is little research in the literature describing the information seeking behavior of open source programmers. This work describes a holistic schema for open- source (OS) programmers??? information seeking, generated through open-coding of questions in OS developer mailing lists. It then reports on a study of the JDT mailing list, showing the types of information sought within this group and, for a subset of the data, it characterizes the responses obtained.

Information-seeking has been recognized as a core subtask in software maintenance. While much of the research in this area attempts to identify the information sought by programmers, the work to date has been typically based on pre-defined information types limiting the potential for new information types to arise. Hence, this paper presents a small empirical study on open-source programmer mailing lists, to iteratively refine a schema of the information that programmers seek based exclusively on an analysis of these programmers’ naturalistic communication. Subsequently it places this schema in the context of the existing literature in the area. The analysis was undertaken on information requests in 288 emails, taken from 2 open source project mailing lists; The Java Bean Scripting Framework (BSF)[1] and the Java Development Tool (JDT)[2]. This work produced a preliminary schema of 3 main categories divided into 18 sub categories.